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<div class="titlepage"><div><div><h5 class="title">
<a name="math_toolkit.stat_tut.weg.cs_eg.chi_sq_intervals"></a><a class="link" href="chi_sq_intervals.html" title="Confidence Intervals on the Standard Deviation">Confidence
          Intervals on the Standard Deviation</a>
</h5></div></div></div>
<p>
            Once you have calculated the standard deviation for your data, a legitimate
            question to ask is "How reliable is the calculated standard deviation?".
            For this situation the Chi Squared distribution can be used to calculate
            confidence intervals for the standard deviation.
          </p>
<p>
            The full example code &amp; sample output is in <a href="../../../../../../example/chi_square_std_dev_test.cpp" target="_top">chi_square_std_dev_test.cpp</a>.
          </p>
<p>
            We'll begin by defining the procedure that will calculate and print out
            the confidence intervals:
          </p>
<pre class="programlisting"><span class="keyword">void</span> <span class="identifier">confidence_limits_on_std_deviation</span><span class="special">(</span>
     <span class="keyword">double</span> <span class="identifier">Sd</span><span class="special">,</span>    <span class="comment">// Sample Standard Deviation</span>
     <span class="keyword">unsigned</span> <span class="identifier">N</span><span class="special">)</span>   <span class="comment">// Sample size</span>
<span class="special">{</span>
</pre>
<p>
            We'll begin by printing out some general information:
          </p>
<pre class="programlisting"><span class="identifier">cout</span> <span class="special">&lt;&lt;</span>
   <span class="string">"________________________________________________\n"</span>
   <span class="string">"2-Sided Confidence Limits For Standard Deviation\n"</span>
   <span class="string">"________________________________________________\n\n"</span><span class="special">;</span>
<span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">setprecision</span><span class="special">(</span><span class="number">7</span><span class="special">);</span>
<span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">setw</span><span class="special">(</span><span class="number">40</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">left</span> <span class="special">&lt;&lt;</span> <span class="string">"Number of Observations"</span> <span class="special">&lt;&lt;</span> <span class="string">"=  "</span> <span class="special">&lt;&lt;</span> <span class="identifier">N</span> <span class="special">&lt;&lt;</span> <span class="string">"\n"</span><span class="special">;</span>
<span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">setw</span><span class="special">(</span><span class="number">40</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">left</span> <span class="special">&lt;&lt;</span> <span class="string">"Standard Deviation"</span> <span class="special">&lt;&lt;</span> <span class="string">"=  "</span> <span class="special">&lt;&lt;</span> <span class="identifier">Sd</span> <span class="special">&lt;&lt;</span> <span class="string">"\n"</span><span class="special">;</span>
</pre>
<p>
            and then define a table of significance levels for which we'll calculate
            intervals:
          </p>
<pre class="programlisting"><span class="keyword">double</span> <span class="identifier">alpha</span><span class="special">[]</span> <span class="special">=</span> <span class="special">{</span> <span class="number">0.5</span><span class="special">,</span> <span class="number">0.25</span><span class="special">,</span> <span class="number">0.1</span><span class="special">,</span> <span class="number">0.05</span><span class="special">,</span> <span class="number">0.01</span><span class="special">,</span> <span class="number">0.001</span><span class="special">,</span> <span class="number">0.0001</span><span class="special">,</span> <span class="number">0.00001</span> <span class="special">};</span>
</pre>
<p>
            The distribution we'll need to calculate the confidence intervals is
            a Chi Squared distribution, with N-1 degrees of freedom:
          </p>
<pre class="programlisting"><span class="identifier">chi_squared</span> <span class="identifier">dist</span><span class="special">(</span><span class="identifier">N</span> <span class="special">-</span> <span class="number">1</span><span class="special">);</span>
</pre>
<p>
            For each value of alpha, the formula for the confidence interval is given
            by:
          </p>
<div class="blockquote"><blockquote class="blockquote"><p>
              <span class="inlinemediaobject"><img src="../../../../../equations/chi_squ_tut1.svg"></span>

            </p></blockquote></div>
<p>
            Where
          </p>
<div class="blockquote"><blockquote class="blockquote"><p>
              <span class="inlinemediaobject"><img src="../../../../../equations/chi_squ_tut2.svg"></span>

            </p></blockquote></div>
<p>
            is the upper critical value, and
          </p>
<div class="blockquote"><blockquote class="blockquote"><p>
              <span class="inlinemediaobject"><img src="../../../../../equations/chi_squ_tut3.svg"></span>

            </p></blockquote></div>
<p>
            is the lower critical value of the Chi Squared distribution.
          </p>
<p>
            In code we begin by printing out a table header:
          </p>
<pre class="programlisting"><span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="string">"\n\n"</span>
        <span class="string">"_____________________________________________\n"</span>
        <span class="string">"Confidence          Lower          Upper\n"</span>
        <span class="string">" Value (%)          Limit          Limit\n"</span>
        <span class="string">"_____________________________________________\n"</span><span class="special">;</span>
</pre>
<p>
            and then loop over the values of alpha and calculate the intervals for
            each: remember that the lower critical value is the same as the quantile,
            and the upper critical value is the same as the quantile from the complement
            of the probability:
          </p>
<pre class="programlisting"><span class="keyword">for</span><span class="special">(</span><span class="keyword">unsigned</span> <span class="identifier">i</span> <span class="special">=</span> <span class="number">0</span><span class="special">;</span> <span class="identifier">i</span> <span class="special">&lt;</span> <span class="keyword">sizeof</span><span class="special">(</span><span class="identifier">alpha</span><span class="special">)/</span><span class="keyword">sizeof</span><span class="special">(</span><span class="identifier">alpha</span><span class="special">[</span><span class="number">0</span><span class="special">]);</span> <span class="special">++</span><span class="identifier">i</span><span class="special">)</span>
<span class="special">{</span>
   <span class="comment">// Confidence value:</span>
   <span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">fixed</span> <span class="special">&lt;&lt;</span> <span class="identifier">setprecision</span><span class="special">(</span><span class="number">3</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">setw</span><span class="special">(</span><span class="number">10</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">right</span> <span class="special">&lt;&lt;</span> <span class="number">100</span> <span class="special">*</span> <span class="special">(</span><span class="number">1</span><span class="special">-</span><span class="identifier">alpha</span><span class="special">[</span><span class="identifier">i</span><span class="special">]);</span>
   <span class="comment">// Calculate limits:</span>
   <span class="keyword">double</span> <span class="identifier">lower_limit</span> <span class="special">=</span> <span class="identifier">sqrt</span><span class="special">((</span><span class="identifier">N</span> <span class="special">-</span> <span class="number">1</span><span class="special">)</span> <span class="special">*</span> <span class="identifier">Sd</span> <span class="special">*</span> <span class="identifier">Sd</span> <span class="special">/</span> <span class="identifier">quantile</span><span class="special">(</span><span class="identifier">complement</span><span class="special">(</span><span class="identifier">dist</span><span class="special">,</span> <span class="identifier">alpha</span><span class="special">[</span><span class="identifier">i</span><span class="special">]</span> <span class="special">/</span> <span class="number">2</span><span class="special">)));</span>
   <span class="keyword">double</span> <span class="identifier">upper_limit</span> <span class="special">=</span> <span class="identifier">sqrt</span><span class="special">((</span><span class="identifier">N</span> <span class="special">-</span> <span class="number">1</span><span class="special">)</span> <span class="special">*</span> <span class="identifier">Sd</span> <span class="special">*</span> <span class="identifier">Sd</span> <span class="special">/</span> <span class="identifier">quantile</span><span class="special">(</span><span class="identifier">dist</span><span class="special">,</span> <span class="identifier">alpha</span><span class="special">[</span><span class="identifier">i</span><span class="special">]</span> <span class="special">/</span> <span class="number">2</span><span class="special">));</span>
   <span class="comment">// Print Limits:</span>
   <span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">fixed</span> <span class="special">&lt;&lt;</span> <span class="identifier">setprecision</span><span class="special">(</span><span class="number">5</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">setw</span><span class="special">(</span><span class="number">15</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">right</span> <span class="special">&lt;&lt;</span> <span class="identifier">lower_limit</span><span class="special">;</span>
   <span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">fixed</span> <span class="special">&lt;&lt;</span> <span class="identifier">setprecision</span><span class="special">(</span><span class="number">5</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">setw</span><span class="special">(</span><span class="number">15</span><span class="special">)</span> <span class="special">&lt;&lt;</span> <span class="identifier">right</span> <span class="special">&lt;&lt;</span> <span class="identifier">upper_limit</span> <span class="special">&lt;&lt;</span> <span class="identifier">endl</span><span class="special">;</span>
<span class="special">}</span>
<span class="identifier">cout</span> <span class="special">&lt;&lt;</span> <span class="identifier">endl</span><span class="special">;</span>
</pre>
<p>
            To see some example output we'll use the <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda3581.htm" target="_top">gear
            data</a> from the <a href="http://www.itl.nist.gov/div898/handbook/" target="_top">NIST/SEMATECH
            e-Handbook of Statistical Methods.</a>. The data represents measurements
            of gear diameter from a manufacturing process.
          </p>
<pre class="programlisting">________________________________________________
2-Sided Confidence Limits For Standard Deviation
________________________________________________

Number of Observations                  =  100
Standard Deviation                      =  0.006278908


_____________________________________________
Confidence          Lower          Upper
 Value (%)          Limit          Limit
_____________________________________________
    50.000        0.00601        0.00662
    75.000        0.00582        0.00685
    90.000        0.00563        0.00712
    95.000        0.00551        0.00729
    99.000        0.00530        0.00766
    99.900        0.00507        0.00812
    99.990        0.00489        0.00855
    99.999        0.00474        0.00895
</pre>
<p>
            So at the 95% confidence level we conclude that the standard deviation
            is between 0.00551 and 0.00729.
          </p>
<h5>
<a name="math_toolkit.stat_tut.weg.cs_eg.chi_sq_intervals.h0"></a>
            <span class="phrase"><a name="math_toolkit.stat_tut.weg.cs_eg.chi_sq_intervals.confidence_intervals_as_a_functi"></a></span><a class="link" href="chi_sq_intervals.html#math_toolkit.stat_tut.weg.cs_eg.chi_sq_intervals.confidence_intervals_as_a_functi">Confidence
            intervals as a function of the number of observations</a>
          </h5>
<p>
            Similarly, we can also list the confidence intervals for the standard
            deviation for the common confidence levels 95%, for increasing numbers
            of observations.
          </p>
<p>
            The standard deviation used to compute these values is unity, so the
            limits listed are <span class="bold"><strong>multipliers</strong></span> for any
            particular standard deviation. For example, given a standard deviation
            of 0.0062789 as in the example above; for 100 observations the multiplier
            is 0.8780 giving the lower confidence limit of 0.8780 * 0.006728 = 0.00551.
          </p>
<pre class="programlisting">____________________________________________________
Confidence level (two-sided)            =  0.0500000
Standard Deviation                      =  1.0000000
________________________________________
Observations        Lower          Upper
                    Limit          Limit
________________________________________
         2         0.4461        31.9102
         3         0.5207         6.2847
         4         0.5665         3.7285
         5         0.5991         2.8736
         6         0.6242         2.4526
         7         0.6444         2.2021
         8         0.6612         2.0353
         9         0.6755         1.9158
        10         0.6878         1.8256
        15         0.7321         1.5771
        20         0.7605         1.4606
        30         0.7964         1.3443
        40         0.8192         1.2840
        50         0.8353         1.2461
        60         0.8476         1.2197
       100         0.8780         1.1617
       120         0.8875         1.1454
      1000         0.9580         1.0459
     10000         0.9863         1.0141
     50000         0.9938         1.0062
    100000         0.9956         1.0044
   1000000         0.9986         1.0014
</pre>
<p>
            With just 2 observations the limits are from <span class="bold"><strong>0.445</strong></span>
            up to to <span class="bold"><strong>31.9</strong></span>, so the standard deviation
            might be about <span class="bold"><strong>half</strong></span> the observed value
            up to <span class="bold"><strong>30 times</strong></span> the observed value!
          </p>
<p>
            Estimating a standard deviation with just a handful of values leaves
            a very great uncertainty, especially the upper limit. Note especially
            how far the upper limit is skewed from the most likely standard deviation.
          </p>
<p>
            Even for 10 observations, normally considered a reasonable number, the
            range is still from 0.69 to 1.8, about a range of 0.7 to 2, and is still
            highly skewed with an upper limit <span class="bold"><strong>twice</strong></span>
            the median.
          </p>
<p>
            When we have 1000 observations, the estimate of the standard deviation
            is starting to look convincing, with a range from 0.95 to 1.05 - now
            near symmetrical, but still about + or - 5%.
          </p>
<p>
            Only when we have 10000 or more repeated observations can we start to
            be reasonably confident (provided we are sure that other factors like
            drift are not creeping in).
          </p>
<p>
            For 10000 observations, the interval is 0.99 to 1.1 - finally a really
            convincing + or -1% confidence.
          </p>
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